Data quality is one of the silent killers of data-driven solutions. If the data is wrong, even a fraction of the time, no one will trust it. So, it’s important to tackle quality as early as possible. Here’s a couple of ways to prevent your project from being killed off: - Build a Strong Data Model – I talk about data modeling a lot and for a good reason. Without a well-structured data model, it’s nearly impossible to ensure your business logic is consistently applied. A solid model helps catch errors and misalignments before they become a bigger problem.
- Enforce Data Constraints – If a line should only have one tracking number, make sure there’s a constraint—either built-in or automated—that prevents duplicates. The same principle applies to data types: ensure that currency fields are formatted as currency, dates as dates, etc.
- Automate Data Quality Checks – There’s nothing worse than an end user pointing out issues in your data. Set up exception reports, alerts, and failure notifications to proactively catch problems in data loads or calculations long before they reach the end users.
Data quality boils down to knowing what your data should look like and putting in checks and balances to make sure that’s what it does look like. All the Best, Tucker |